# Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/1905.07719 · HTML: https://mlsanity.com/paper/1905.07719/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2019.

## Built on by (most cited first, 9 of 9)

- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Solving Aspect Category Sentiment Analysis as a Text Generation Task](https://mlsanity.com/paper/2110.07310/) (2021)
- [Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network](https://mlsanity.com/paper/2108.02352/) (2021)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning](https://mlsanity.com/paper/2205.10970/) (2022)
- [A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach](https://mlsanity.com/paper/2212.10051/) (2022)
- [Aspect-Sentiment-Multiple-Opinion Triplet Extraction](https://mlsanity.com/paper/2110.07303/) (2021)
- [Dual-Attention Model for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/2303.07689/) (2023)
- [LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset](https://mlsanity.com/paper/2604.10417/) (2026)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.